7 citations · 19 across the 6 of their papers we have counts for
7 papers
Rethinking Semantic Alignment in LLM-Enhanced Collaborative Filtering: A Spectral Decoupling Approach
Yedong Jin, Shaowen Peng, Tsunenori Mine +2
Recent advances in LLM-enhanced recommendation commonly align semantic representations with collaborative embeddings in a shared space, yet how alignment affects LLM-encoded inform…
How Powerful is Graph Filtering for Recommendation
Shaowen Peng, Xin Liu, Kazunari Sugiyama +1
It has been shown that the effectiveness of graph convolutional network (GCN) for recommendation is attributed to the spectral graph filtering. Most GCN-based methods consist of a…
Balancing Embedding Spectrum for Recommendation
Shaowen Peng, Kazunari Sugiyama, Xin Liu +1
Modern recommender systems heavily rely on high-quality representations learned from high-dimensional sparse data. While significant efforts have been invested in designing powerfu…
Privacy-Preserving Sequential Recommendation with Collaborative Confusion
Wei Wang, Yujie Lin, Pengjie Ren +7
Sequential recommendation has attracted a lot of attention from both academia and industry, however the privacy risks associated to gathering and transferring users' personal inter…
SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation
Shaowen Peng, Kazunari Sugiyama, Tsunenori Mine
With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance. However, most GCN-bas…
Less is More: Reweighting Important Spectral Graph Features for Recommendation
Shaowen Peng, Kazunari Sugiyama, Tsunenori Mine
As much as Graph Convolutional Networks (GCNs) have shown tremendous success in recommender systems and collaborative filtering (CF), the mechanism of how they, especially the core…
A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering
Shaowen Peng, Tsunenori Mine
Graph Convolutional Network (GCN) has achieved great success and has been applied in various fields including recommender systems. However, GCN still suffers from many issues such…